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The Science of Smarter Predictions : Sports analysis

About the Course:

The field of predictive analysis in computer science is a rapidly developing area within Artificial Intelligence. Predictive analysis (predictive analytics) refers to the use of algorithms, data, and computational models to forecast future outcomes based on historical data.

This course introduces the fundamentals of predictive analysis through applications in sports analytics and prediction systems. For instance, can we estimate the probability of a team winning based on past performance and gameplay data?

The programme is designed for students interested in Statistics, Probability Theory, Data Science, and Economics—as well as those curious about how platforms such as Kalshi, Polymarket, or ESPN Analytics operate.

Through this programme, students will engage directly with a research-driven environment—building predictive models, exploring new markets, and developing features that can be deployed on a live platform.

Lead Mentor:

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Nwanacho Nwana is a graduate from the Massacutues Insttute of Technology, an independent researcher and author. He has previously received grant funding from MIT's Integrated Learning Initiative and has a track record of presenting at MIT and Columbia conferences.

Starting Date:

April 25, 2026

Eligibility:

High school and above, with demonstrated interest in Computer Science and Probability. 

Timings:

Mondays, Tuesdays and Thursdays | 6.30 PM IST | 1.5 hours

Application deadline

April 20 , 2026.

Prerequisites

Students are expected to have a foundational background in computer science, along with a working knowledge of Python and its core libraries (such as NumPy and pandas). A basic understanding of probability theory and statistics is essential, as the course builds on these concepts to develop and evaluate predictive models.

Who This Is For

This course is designed for students who are curious about:

  • Statistics, probability, and data science

  • Economics, forecasting, and prediction markets

  • How platforms like Kalshi, Polymarket, or ESPN Analytics operate

  • Building systems that go live and generate real-world signals—not just static reports

  • Conducting research that can be published, cited, or extended into future academic or professional work

Project Focus:

In the duration of the course, you are required to. Select any one market to create prediction models for:

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Register now

Sign up for this course by using the registration link below. Seats are limited to between 8-12 and guaged based on student's past perfomances.


 

Fee

NPR 38,000

Up to two scholarships of 100%  are available based on the answers provided in the response form. The course fee covers three days of classes per week.

 

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